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JAIST Repository >
科学技術開発戦略センター 2003～2008 >
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IFSR 2005 >
このアイテムの引用には次の識別子を使用してください:
http://hdl.handle.net/10119/3954
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| タイトル: | Ensemble Learning with Neural Networks for Classifying Environmental Sounds |
| 著者: | Hiramatsu, Ayako
Simotaki, Asato
Nose, Kazuo
Minakata, Toshio
Tennmoku, Kenji
Hattori, Osamu |
| キーワード: | Traffic sounds
Ensemble learning
Neural networks
Bagging
Boosting |
| 発行日: | Nov-2005 |
| 出版者: | JAIST Press |
| 抄録: | This paper proposes a classification method for environmental sounds based on neural networks. However, neural networks need trail and error, which are very tedious tasks. To simplify classification accuracy, we investigate two popular ensemble learning methods: Bagging and AdaBoost. We experimentally compare their performances with a single neural network. The results show that their performance is slightly improved and that bagging works more effectively than AdaBoost. |
| 記述: | The original publication is available at JAIST Press http://www.jaist.ac.jp/library/jaist-press/index.html
IFSR 2005 : Proceedings of the First World Congress of the International Federation for Systems Research : The New Roles of Systems Sciences For a Knowledge-based Society : Nov. 14-17, 2164, Kobe, Japan
Symposium 3, Session 8 : Intelligent Information Technology and Applications Computational Intelligence (2) |
| 言語: | ENG |
| URI: | http://hdl.handle.net/10119/3954 |
| ISBN: | 4-903092-02-X |
| 出現コレクション: | IFSR 2005
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このアイテムのファイル:
| ファイル |
記述 |
サイズ | 形式 |
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| 20190.pdf | | 125Kb | Adobe PDF | 見る/開く |
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